Application of data protection laws with a proposal for a flexible regime for humanitarian organisations
Bibliographic record
Abstract
Humanitarian organisations often operate in emergency contexts where strict compliance with data protection laws, such as the General Data Protection Regulation (GDPR), can pose significant practical challenges. This paper explores the need for a differentiated data protection regime tailored to the realities of humanitarian crises, balancing efficiency and the fundamental rights of data subjects. By analysing key European Court of Justice cases, including Schrems II (C-311/18), Nowak (C-434/16) and Pankki S (C-579/21), the paper highlights the importance of adapting core GDPR principles to crisis situations. It also examines the integration of human rights principles, emphasising the protection of dignity and autonomy during emergencies. Furthermore, it addresses regulatory challenges, proposing proactive engagement with authorities to ensure accountability and trust. Practical solutions are proposed such as simplified Data Protection Impact Assessments (DPIAs), the use of pseudonymisation, data minimisation and standardised Memorandums of Understanding (MOUs) to replace complex contractual requirements. These measures aim to ensure compliance while enabling rapid and effective responses in emergencies. The paper concludes by calling for the development of a flexible regulatory framework that integrates data protection into the operational needs of humanitarian organisations without compromising ethical and legal standards.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.093 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.007 | 0.039 |
| Scholarly communication | 0.021 | 0.023 |
| Open science | 0.007 | 0.016 |
| Research integrity | 0.038 | 0.030 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".